hindi_model_with_lm_vakyansh
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Language | Hindi |
| Architecture | wav2vec2 with Language Model |
| Best WER | 17.4% (Common Voice 7.0) |
What is hindi_model_with_lm_vakyansh?
hindi_model_with_lm_vakyansh is an advanced Automatic Speech Recognition (ASR) model specifically designed for Hindi language processing. Developed by Harveenchadha, it leverages the wav2vec2 architecture enhanced with a language model to achieve state-of-the-art performance in Hindi speech recognition.
Implementation Details
The model is built using PyTorch and Transformers frameworks, incorporating both acoustic and language modeling capabilities. It has been extensively tested across multiple versions of the Common Voice dataset, demonstrating consistent performance with Word Error Rates (WER) ranging from 17.4% to 19.14%.
- Trained on Harveenchadha/indic-voice dataset
- Implements wav2vec2 architecture with language model integration
- Optimized for Hindi language processing
- Achieves 5.93-8.91% Character Error Rate (CER)
Core Capabilities
- High-accuracy Hindi speech recognition
- Robust performance across different Common Voice versions
- Production-ready with inference endpoints support
- Suitable for both academic and commercial applications
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its specialized optimization for Hindi language processing and its impressive performance metrics across different Common Voice dataset versions. The integration of a language model with wav2vec2 architecture provides enhanced accuracy for real-world applications.
Q: What are the recommended use cases?
This model is ideal for Hindi speech recognition tasks, including transcription services, voice command systems, and automated subtitling. It's particularly suitable for applications requiring robust Hindi language understanding with production-grade performance.
